Combined person tracking and classification in a network of chemical sensors

Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call the security personnel in case of anomalies. We are describing the concept and the realization of an indoor security assistance system for real-time decision support. Data for the classification of persons are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and assign it to a person. We compensate for this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Both tracking and fusion of tracks with chemical attributes can be processed within one framework called probabilistic multiple hypothesis tracking (PMHT). An extension of PMHT for dealing with classification measurements (PMHT-c) already exists. We show how PMHT-c can be applied to assign chemical attributes to person tracks. This affords the localization of threads and a timely notification of the security personnel.